國立虎尾科技大學 |

AI in banking = practical applications and case studies /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	AI in banking/ by Liyu Shao, Qin Chen, Min He.
其他題名:	practical applications and case studies /
作者:	Shao, Liyu.
其他作者:	Chen, Qin.
出版者:	Singapore :Springer Nature Singapore : : 2025.,
面頁冊數:	xxii, 354 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Banks and banking - Technological innovations. -
電子資源:	https://doi.org/10.1007/978-981-96-3837-6
ISBN:	9789819638376

AI in banking = practical applications and case studies /
Shao, Liyu.

AI in bankingpractical applications and case studies /[electronic resource] :by Liyu Shao, Qin Chen, Min He. - Singapore :Springer Nature Singapore :2025. - xxii, 354 p. :ill. (some col.), digital ;24 cm.

Part I: Smart Marketing -- Chapter 1. Mobile Banking Potential Monthly Active Customer Mining: Automated Machine Learning Techniques -- Chapter 2. Retail Potential High-value Customer Identification: Graph Neural Network Technology -- Chapter 3. Accurate Recommendation for Banking: Recommender System -- Chapter 4. Assessing the Value of Bank Online Marketing Posts: Reinforcement Learning Techniques -- Chapter 5: Modeling Binary Causal Effects of Related Repayments: Causal Inference Techniques -- Part II: Intelligent Risk Control -- Chapter 6. Telecom Fraud Money Laundering Account Recognition Case: Multiple Machine Learning Techniques -- Chapter 7. Developing a Dialectal Speech Phone Collection Bimodal Robot from Scratch: Intelligent Voice Q&A Technology -- Chapter 8. Chattel Collateral Warehouse Visual Monitoring Project: Image Understanding Technology -- Chapter 9. Personal Loan Delinquency Prediction Project: Bayesian Network Techniques -- Part III: Intelligent Operation -- Chapter 10. Enterprise WeChat Private Traffic Customer Cold Start Program: Automated Control Technology -- Chapter 11 Intelligent Inspection Robot for Commercial Bank Data Centers: Computer Vision Technology.

Big data and artificial intelligence (AI) cannot remain limited to academic theoretical research. It is crucial to utilize them in practical business scenarios, enabling cutting-edge technology to generate tangible value. This book delves into the application of AI from theory to practice, offering detailed insights into AI project design and code implementation across eleven business scenarios in four major sectors: retail banking, e-banking, bank credit, and tech operations. It provides hands-on examples of various technologies, including automatic machine learning, integrated learning, graph computation, recommendation systems, causal inference, generative adversarial networks, supervised learning, unsupervised learning, computer vision, reinforcement learning, fuzzy control, automatic control, speech recognition, semantic understanding, Bayesian networks, edge computing, and more. This book stands as a rare and practical guide to AI projects in the banking industry. By avoiding complex mathematical formulas and theoretical analyses, it uses plain language to illustrate how to apply AI technology in commercial banking business scenarios. With its strong readability and practical approach, this book enables readers to swiftly develop their own AI projects.

ISBN: 9789819638376

Standard No.: 10.1007/978-981-96-3837-6doiSubjects--Topical Terms:

657856
Banks and banking
--Technological innovations.

LC Class. No.: HG1709

Dewey Class. No.: 332.10285

AI in banking = practical applications and case studies /
LDR:03476nam a2200325 a 4500 001 1161760
003 DE-He213
005 20250410135641.0
006 m d
007 cr nn 008maaau
008 251029s2025 si s 0 eng d
020 $a 9789819638376 $q (electronic bk.)
020 $a 9789819638369 $q (paper)
024 7 $a 10.1007/978-981-96-3837-6 $2 doi
035 $a 978-981-96-3837-6
040 $a GP $c GP
041 0 $a eng
050 4 $a HG1709
072 7 $a UYQM $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 332.10285 $2 23
090 $a HG1709 $b .S528 2025
100 1 $a Shao, Liyu. $3 1488726
245 1 0 $a AI in banking $h [electronic resource] : $b practical applications and case studies / $c by Liyu Shao, Qin Chen, Min He.
260 $a Singapore : $c 2025. $b Springer Nature Singapore : $b Imprint: Springer,
300 $a xxii, 354 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Part I: Smart Marketing -- Chapter 1. Mobile Banking Potential Monthly Active Customer Mining: Automated Machine Learning Techniques -- Chapter 2. Retail Potential High-value Customer Identification: Graph Neural Network Technology -- Chapter 3. Accurate Recommendation for Banking: Recommender System -- Chapter 4. Assessing the Value of Bank Online Marketing Posts: Reinforcement Learning Techniques -- Chapter 5: Modeling Binary Causal Effects of Related Repayments: Causal Inference Techniques -- Part II: Intelligent Risk Control -- Chapter 6. Telecom Fraud Money Laundering Account Recognition Case: Multiple Machine Learning Techniques -- Chapter 7. Developing a Dialectal Speech Phone Collection Bimodal Robot from Scratch: Intelligent Voice Q&A Technology -- Chapter 8. Chattel Collateral Warehouse Visual Monitoring Project: Image Understanding Technology -- Chapter 9. Personal Loan Delinquency Prediction Project: Bayesian Network Techniques -- Part III: Intelligent Operation -- Chapter 10. Enterprise WeChat Private Traffic Customer Cold Start Program: Automated Control Technology -- Chapter 11 Intelligent Inspection Robot for Commercial Bank Data Centers: Computer Vision Technology.
520 $a Big data and artificial intelligence (AI) cannot remain limited to academic theoretical research. It is crucial to utilize them in practical business scenarios, enabling cutting-edge technology to generate tangible value. This book delves into the application of AI from theory to practice, offering detailed insights into AI project design and code implementation across eleven business scenarios in four major sectors: retail banking, e-banking, bank credit, and tech operations. It provides hands-on examples of various technologies, including automatic machine learning, integrated learning, graph computation, recommendation systems, causal inference, generative adversarial networks, supervised learning, unsupervised learning, computer vision, reinforcement learning, fuzzy control, automatic control, speech recognition, semantic understanding, Bayesian networks, edge computing, and more. This book stands as a rare and practical guide to AI projects in the banking industry. By avoiding complex mathematical formulas and theoretical analyses, it uses plain language to illustrate how to apply AI technology in commercial banking business scenarios. With its strong readability and practical approach, this book enables readers to swiftly develop their own AI projects.
650 0 $a Banks and banking $x Technological innovations. $3 657856
650 0 $a Artificial intelligence $x Financial applications. $3 1346102
650 1 4 $a Machine Learning. $3 1137723
650 2 4 $a Data Science. $3 1174436
650 2 4 $a Computer Vision. $3 1127422
650 2 4 $a Natural Language Processing (NLP). $3 1254293
650 2 4 $a Biometrics. $3 677095
650 2 4 $a Python. $3 1115944
700 1 $a Chen, Qin. $3 1488727
700 1 $a He, Min. $3 1488728
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-96-3837-6
950 $a Computer Science (SpringerNature-11645)